Euan Analysis

library(plotly)
library(e1071)

source(file = "cleaning.R")

Rows: 128992 Columns: 18── Column specification ────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (12): Quarter, QuarterQF, HB, HBQF, Location, LocationQF, AdmissionType, AdmissionTypeQF, Sex, A...
dbl  (6): Episodes, LengthOfEpisode, AverageLengthOfEpisode, Stays, LengthOfStay, AverageLengthOfStay
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Rows: 40711 Columns: 18── Column specification ────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (11): Quarter, QuarterQF, HB, HBQF, Location, LocationQF, AdmissionType, AdmissionTypeQF, SIMDQF...
dbl  (7): SIMD, Episodes, LengthOfEpisode, AverageLengthOfEpisode, Stays, LengthOfStay, AverageLengt...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.Rows: 5 Columns: 8── Column specification ────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Country
dbl (7): Date, SIMDQuintile, TotalPositive, CrudeRatePositive, FirstInfections, Reinfections, Percen...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.`summarise()` has grouped output by 'simd'. You can override using the `.groups` argument.`summarise()` has grouped output by 'simd'. You can override using the `.groups` argument.`summarise()` has grouped output by 'simd'. You can override using the `.groups` argument.

inputs <- list(
  admission_input = "Emergency Inpatients",
  sex_input = "Male",
  age_input = "40-49 years",
  simd_input = 5,
  age_sex = "age",
  lockdown_input = "post"
  
  
)

bot_simd_clean %>% 
  filter(admission_type == inputs$admission_input) %>% 
  group_by(simd) %>% 
  summarise(total_episodes = sum(episodes)) %>% 
  ggplot()+
  aes(x = simd,
      y = total_episodes,
      fill = as.factor(simd)) +
  geom_col() +
  theme_minimal()+
  labs(
    x = "SIMD Level",
    y = "Total Episodes"
  ) +
  theme(
    legend.position = "none"
  )

bot_sex_clean %>% 
  group_by(age,
           sex) %>% 
  summarise(total_episodes = sum(episodes)) %>% 
  ggplot()+
  aes(x = age,
      y = total_episodes,
      fill = sex) +
  geom_col(position = "dodge") +
  theme_minimal()+
  labs(
    x = "\nAge Ranges",
    y = "Total Episodes\n"
  ) +
  theme(
    legend.position = "none",
    axis.text.x = element_text(angle = 315,
                               vjust = 0.2,
                               hjust = 0.5)
  )

`summarise()` has grouped output by 'age'. You can override using the `.groups` argument.

bot_sex_clean %>% 
  filter(age == inputs$age_input,
         admission_type %in% c("Elective Inpatients",
                               "Emergency Inpatients")) %>% 
  group_by(age, admission_type) %>% 
  summarise(tot_episodes = sum(episodes)) %>% 
  ggplot()+
  aes(x = age,
      y = tot_episodes,
      fill = admission_type) +
  geom_col(position = "dodge")

`summarise()` has grouped output by 'age'. You can override using the `.groups` argument.

simd_stack <- ggplotly(bot_simd_clean %>% 
  drop_na(simd) %>% 
  filter(admission_type %in% c("Elective Inpatients",
                               "Emergency Inpatients"),
         lockdown == inputs$lockdown_input) %>% 
  group_by(simd, admission_type) %>% 
  summarise(total_episodes = sum(episodes)) %>% 
  ggplot()+
  aes(x = simd,
      y = total_episodes,
      fill = admission_type) +
  geom_col(position = "stack")+
  theme_minimal(
  ) +
  labs(
    x = "\nSIMD Ranking",
    y = "Total Episodes\n",
    fill = "Admission Type"
  ))

`summarise()` has grouped output by 'simd'. You can override using the `.groups` argument.

bot_sex_clean %>% 
  group_by(sex, quarter) %>% 
  summarise(mean_episodes = mean(episodes)) %>% 
  ggplot() +
  aes(x = quarter,
      y = mean_episodes,
      group = sex,
      colour = sex) +
  geom_line() +
  theme(
    axis.text.x = element_text(angle = 315,
                               vjust = 0.2,
                               hjust = 0.5)
  
    )

`summarise()` has grouped output by 'sex'. You can override using the `.groups` argument.

NA
NA

bot_simd_clean %>% 
  drop_na(simd) %>% 
  filter(admission_type %in% c("Elective Inpatients",
                               "Emergency Inpatients")) %>% 
  group_by(simd, admission_type) %>% 
  summarise(mean_episodes = mean(episodes)) %>% 
  ggplot()+
  aes(x = simd,
      y = mean_episodes,
      fill = admission_type) +
  geom_col(position = "stack")

`summarise()` has grouped output by 'simd'. You can override using the `.groups` argument.

bot_simd_clean %>% 
  drop_na(simd) %>% 
  filter(admission_type %in% c("Elective Inpatients",
                               "Emergency Inpatients")) %>% 
  group_by(simd, admission_type) %>% 
  summarise(mean_episodes = sum(episodes))

`summarise()` has grouped output by 'simd'. You can override using the `.groups` argument.

ggplotly(covid_simd %>% 

  ggplot() +
    aes(
      x = simd_quintile,
      y = first_infections,
      fill = as.factor(simd_quintile),
      text = first_infections
    ) +
    geom_col()
)

ggplotly(covid_simd %>% 

  ggplot() +
    aes(
      x = simd_quintile,
      y = reinfections,
      text = reinfections
    ) +
    geom_col(fill = "blue") +
    theme_minimal(),
  tooltip = "text"
)

bot_sex_clean %>% 
  filter(age == "0-9 years") %>% 
  group_by(sex)